US20180365555A1 - Artificial intelligence based algorithm for predicting pipeline leak and corrosion detection - Google Patents
Artificial intelligence based algorithm for predicting pipeline leak and corrosion detection Download PDFInfo
- Publication number
- US20180365555A1 US20180365555A1 US15/840,535 US201715840535A US2018365555A1 US 20180365555 A1 US20180365555 A1 US 20180365555A1 US 201715840535 A US201715840535 A US 201715840535A US 2018365555 A1 US2018365555 A1 US 2018365555A1
- Authority
- US
- United States
- Prior art keywords
- pipeline
- corrosion
- data
- model
- parameters associated
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Abandoned
Links
Images
Classifications
-
- G06N3/0436—
-
- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F17—STORING OR DISTRIBUTING GASES OR LIQUIDS
- F17D—PIPE-LINE SYSTEMS; PIPE-LINES
- F17D5/00—Protection or supervision of installations
-
- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F17—STORING OR DISTRIBUTING GASES OR LIQUIDS
- F17D—PIPE-LINE SYSTEMS; PIPE-LINES
- F17D5/00—Protection or supervision of installations
- F17D5/02—Preventing, monitoring, or locating loss
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/043—Architecture, e.g. interconnection topology based on fuzzy logic, fuzzy membership or fuzzy inference, e.g. adaptive neuro-fuzzy inference systems [ANFIS]
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
- G06N3/084—Backpropagation, e.g. using gradient descent
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
- G06N3/086—Learning methods using evolutionary algorithms, e.g. genetic algorithms or genetic programming
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N5/00—Computing arrangements using knowledge-based models
- G06N5/01—Dynamic search techniques; Heuristics; Dynamic trees; Branch-and-bound
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N5/00—Computing arrangements using knowledge-based models
- G06N5/04—Inference or reasoning models
- G06N5/048—Fuzzy inferencing
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N5/00—Computing arrangements using knowledge-based models
- G06N5/04—Inference or reasoning models
- G06N5/045—Explanation of inference; Explainable artificial intelligence [XAI]; Interpretable artificial intelligence
Definitions
- the present invention relates to an Artificial Intelligence (Al) based algorithm for the detection and prediction of pipeline leaks and corrosion.
- Al Artificial Intelligence
- Natural gas is a growing industry and is rapidly replacing oil and coal as a cleaner and lower cost fuel.
- the shale revolution has boosted the importance of natural gas to the energy landscape, particularly in the US.
- the natural gas delivery infrastructure is aging and deteriorating.
- the US Department of Energy has emphasized that having a reliable natural gas delivery system is one of the critical needs for ensuring the growth and function of the energy sector.
- the invention provides a method for predicting corrosion along the length of a pipeline as well as corresponding leak profiles.
- the current prediction methods provide only a generalized corrosion average for the pipeline as a whole, but do not provide accurate or detailed information for specific problem areas.
- the invention overcomes this problem and provides a more complete predictive model that distinguishes different sites along the pipeline length and highlights those locations along the pipeline that have higher and lower chance of corrosion and ultimate failure. This results in a more precise model that enables proactive maintenance of the pipeline and thereby increases reliability while lowering the cost of operation.
- a method for predicting corrosion in a pipeline wherein the pipeline is subject to corrosion comprising the steps of obtaining data of parameters associated with the pipeline, comparing the data against previously generated data of parameters associated with the pipeline, and calculating, based on differences between the data with the previously generated data the probability of corrosion in the pipeline.
- the probability of corrosion is typically for a location along the pipeline.
- the pipeline will typically contain a hydrocarbon selected from the group consisting of liquid hydrocarbons and natural gas.
- the data of parameters associated with the pipeline are selected from the group consisting of external stresses and internal stresses.
- the external stresses are selected from the group consisting of elevations, inclinations, weather patterns, external temperatures.
- the internal stresses are selected from the group consisting of hydrocarbon composition, gas composition, pressure, flow rates, fluid and gas and hydrocarbon velocities.
- the predicting of the probability of corrosion in the pipeline is performed by an artificial intelligence algorithm.
- the previously generated data of parameters is continuously updated for the pipeline.
- the artificial intelligence algorithm is a continuously updated predictive model.
- the artificial intelligence algorithm is selected from the group consisting of De-Waard Model, Norsok Model and the Leak Rate Model.
- the generic algorithm interacts with an artificial neural network in a continuous manner to generate the prediction of leaks and corrosion rates in the pipeline.
- the predictions are further refined in a fuzzy logic subroutine algorithm.
- the fuzzy logic subroutine algorithm is a type 2 fuzzy logic system.
- the type 2 fuzzy logic system comprises fuzzification, a fuzzy inference process and defuzzification.
- the method of the invention takes into consideration a number of measured parameters associated with the pipeline and then compares these measurements against past data to predict the probability of corrosion and potential leakage sites.
- An important aspect of the method of the invention is that it is based on an Al algorithm. This makes the predictive function more accurate and reliable because the past data used for comparison is updated on a continuous basis as additional data and experience is obtained. Therefore, the method of the invention provides a continuously updated predictive model and provides better and continuously improving reliability standards.
- stresses on a pipeline that can change corrosion rates and corresponding leak sites along the length of the pipeline. These stresses may be external, such as environmental differences, or internal, such as compositional differences of the pipeline materials or of the material being transported. Different environments are generally related to the physical location of the pipeline and may result from different elevations, inclinations, general weather patterns, temperatures, etc.
- the material differences can include gas composition, pressure, flow rates, fluid and gas velocities, etc.
- the Figure is a flow chart showing the data and previously generated data being evaluated by various algorithmic parameters to compare differences.
- a number of modeling methods may be used to perform the comparison. These modeling methods include the De-Waard Model, the Norsok Model and the Leak Rate Model.
- the model of De-Waard & co-workers was developed based on carbon dioxide corrosion prediction in pipelines. For many years this was the only and most widely used model for the prediction of worst case scenario of carbon dioxide transport. This model is based on empirical fitting of laboratory experimental data and has been revised several times in the last 25 years as new information became available. Several different correction factors have been added to the original equation for different possible scenarios of materials and presence of moisture, PH, corrosion products and oil/liquid wetting. This model does not account for the use of protective corrosion films. Several Operating & Oil & Gas companies have created adaptations of this model and are actively using it in the field for making predictions such as BPs' Cassandra tool. Though this is a good first line model, the performance in predicting corrosion is not very satisfactory.
- the commonly used De-Waard 95 model failed to predict the corrosion rate for 74.3% of excavation points. So 86 out of 116 excavation points yielded an absolute error greater than 0.05 mm/yr.
- the Norsok M-506 model is an empirical model developed by Norwegian Oil company StetOil. This model is fitted to a large amount of laboratory data and provides a predictive equation for the assessment of corrosion. This model extends the range of predictions and can be used for higher temperatures (150° C.) and higher PH values. But like the De-Waard model, it has serious limitations in predicting corrosion along the pipeline.
- the leak rate models are based on accounting for a multitude of phenomenon in the transport of hydrocarbons such as diffusion theory, fluid mechanics, numerical methods, medium flow state as well the characteristics and nature of leakage holes or spots. These different underlying transport phenomena are modeled and solved through appropriate numerical methods. For example, a hole-tube integrated model has been used to predict the media leakage rate for long-distance NG pipelines. These models are used for risk assessment and integrity management of onshore pipelines but they have serious limitations as far accuracy and reliability is concerned. Therefore, operators are forced to use other available methods like pigging, LDAR, etc., for ensuring a proper risk assessment and integrity management.
- the invention can also be explained with reference to the Figure, which shows a schematic representation of the method.
- the input from one or more sources as listed in the Input Data box of the Figure are initially gathered.
- the physical data may be gathered from sensors or the like deployed along the length of the pipeline.
- the gathered data is then compared to past known data stored for example in a database. If the gathered data is “normal” meaning that the data is consistent with past data points, then corrosion rates and leak rates can be predicted by using know deterministic models, such as the De-Waard model.
- the method of the invention utilizes a learning network in order to update the database and thereby increase the reliability and predictability for the system.
- the Figure shows the proposed architecture and method to predict corrosion and leak rates according to the invention.
- ANN Artificial Neural Network
- NN Artificial Neural Network
- the basic or input layer is fed with selected input variables which are then passed into the hidden layers in which the processing will take place. For example, algorithms such as the Levenberg-Marquardt or Back-Propagation algorithms may be applied.
- the processing takes place in these hidden layers and as output is generated the last hidden layer communicates the results to an external source which generates an output vector of possible disorders such as difference in leak rates, corrosion rates, pressures, flow rates etc.
- the performance of the ANN is dependent upon the number of layers, the number of neurons in each layer, the weights between the related neurons and threshold. These parameters are obtained through subsequent training of the ANN system.
- the training of an ANN system is a complex task and could lead to predictions which are not relevant. Therefore, according to the invention, the parameters are computed by an intelligent optimization algorithm of evolutionary origin, for example, a Genetic Algorithm (GA).
- GA Genetic Algorithm
- the constant interaction of the GA with the ANN system ensures minimization of error and more realistic computation of parameters required for the ANN system.
- the output from this hybrid system provides the leak and corrosion rates along the pipeline. This output may be sent directly to an operator to make decisions about risks and asset integrity.
- a Fuzzy logic subroutine is activated and data is further scrutinized for better and crisper predictions. This can be carried out using a T2FL (type 2 fuzzy logic system).
- the T2FL system is made up of a Fuzzifier, a Rule based inference engine and output processor. Therefore, the system integrates the experience and knowledge of very experienced human pipeline operators/experts into the parameters associated with the rule base and membership functions. This helps less experienced operators with key decision making processes.
- the fuzzy logic system can be thought of as an average human beings' feelings and inference processing.
- the fuzzy logic system or strategy is a range-to-point or range-to-range control that differs from the standard control theory of point-to-point control.
- the output of a fuzzy controller is derived from fuzzifications of both inputs and outputs using the associated member functions. A crisp input will be converted into different members of associated member functions, which than is processed as a range of inputs.
- the fuzzy logic system implemented by the invention here has three main components.
- Fuzzification Conversion of classical data or crisp data into fuzzy data or membership function
- Fuzzy Inference Process Combines membership functions with rules to derive the fuzzy output
- Defuzzification Uses different methods to compute each associated output and puts them into a lookup table, with the process then picking an output from the lookup table based on current inputs from the ANN system.
- the output from the method of the invention may be used to predict corrosion rates and possible specific leak sites along a pipeline. This then allows more accurate preventive maintenance to be performed that can serve to avoid an actual leak from occurring.
- the method of the invention therefore provides a way of preventing leaks from occurring. This then results in significant cost savings as well as avoiding additional issues that can arise if a leak occurs, e.g. environmental pollution, loss of natural gas, shut down and repair costs, etc.
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- Software Systems (AREA)
- General Physics & Mathematics (AREA)
- Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Computational Linguistics (AREA)
- Evolutionary Computation (AREA)
- Computing Systems (AREA)
- Data Mining & Analysis (AREA)
- Mathematical Physics (AREA)
- Artificial Intelligence (AREA)
- General Health & Medical Sciences (AREA)
- Molecular Biology (AREA)
- Biophysics (AREA)
- Biomedical Technology (AREA)
- Mechanical Engineering (AREA)
- Bioinformatics & Computational Biology (AREA)
- Evolutionary Biology (AREA)
- Physiology (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Automation & Control Theory (AREA)
- Fuzzy Systems (AREA)
- Computational Mathematics (AREA)
- Mathematical Analysis (AREA)
- Mathematical Optimization (AREA)
- Pure & Applied Mathematics (AREA)
- Pipeline Systems (AREA)
Abstract
A method for predicting corrosion in a pipeline wherein the pipeline is subject to corrosion by the steps of obtaining data of parameters associated with the pipeline, comparing the data against previously generated data of parameters associated with the pipeline, and calculating, based on differences between the data with the previously generated data the probability of corrosion in the pipeline. The calculation can be performed using a variety of algorithms and modeling methods.
Description
- This application claims priority from U.S. provisional patent application 62/437,716 filed on Dec. 22, 2016
- The present invention relates to an Artificial Intelligence (Al) based algorithm for the detection and prediction of pipeline leaks and corrosion.
- Natural gas is a growing industry and is rapidly replacing oil and coal as a cleaner and lower cost fuel. The shale revolution has boosted the importance of natural gas to the energy landscape, particularly in the US. However, the natural gas delivery infrastructure is aging and deteriorating. The US Department of Energy has emphasized that having a reliable natural gas delivery system is one of the critical needs for ensuring the growth and function of the energy sector. There are approximately 650 thousand miles of delivery pipelines current in existence in the US. Timely detection of failures, caused by corrosion or leaks is a critical element of ensuring the reliability of the natural gas delivery systems.
- There is a need for improvements in the field of detecting and predicting natural gas pipeline leaks or failures, in order to increase the stability and reliability of the pipeline infrastructure.
- The invention provides a method for predicting corrosion along the length of a pipeline as well as corresponding leak profiles. The current prediction methods provide only a generalized corrosion average for the pipeline as a whole, but do not provide accurate or detailed information for specific problem areas. The invention overcomes this problem and provides a more complete predictive model that distinguishes different sites along the pipeline length and highlights those locations along the pipeline that have higher and lower chance of corrosion and ultimate failure. This results in a more precise model that enables proactive maintenance of the pipeline and thereby increases reliability while lowering the cost of operation.
- In a first embodiment of the invention, there is disclosed a method for predicting corrosion in a pipeline wherein the pipeline is subject to corrosion comprising the steps of obtaining data of parameters associated with the pipeline, comparing the data against previously generated data of parameters associated with the pipeline, and calculating, based on differences between the data with the previously generated data the probability of corrosion in the pipeline. The probability of corrosion is typically for a location along the pipeline.
- The pipeline will typically contain a hydrocarbon selected from the group consisting of liquid hydrocarbons and natural gas.
- The data of parameters associated with the pipeline are selected from the group consisting of external stresses and internal stresses. The external stresses are selected from the group consisting of elevations, inclinations, weather patterns, external temperatures. The internal stresses are selected from the group consisting of hydrocarbon composition, gas composition, pressure, flow rates, fluid and gas and hydrocarbon velocities.
- The predicting of the probability of corrosion in the pipeline is performed by an artificial intelligence algorithm. The previously generated data of parameters is continuously updated for the pipeline. The artificial intelligence algorithm is a continuously updated predictive model.
- The artificial intelligence algorithm is selected from the group consisting of De-Waard Model, Norsok Model and the Leak Rate Model.
- If the data of parameters associated with the pipeline is consistent with previously generated data of parameters associated with the pipeline, then this is a normal condition and corrosion and leak rates are predicted using a deterministic model such as the De-Waard Model.
- If the data of parameters associated with the pipeline is not consistent with previously generated data of parameters associated with the pipeline then this is a learning condition and corrosion and leak rates are predicted based upon a generic algorithm.
- The generic algorithm interacts with an artificial neural network in a continuous manner to generate the prediction of leaks and corrosion rates in the pipeline.
- The predictions are further refined in a fuzzy logic subroutine algorithm. The fuzzy logic subroutine algorithm is a
type 2 fuzzy logic system. Thetype 2 fuzzy logic system comprises fuzzification, a fuzzy inference process and defuzzification. - During normal operation, oil and gas pipelines are subjected to both internal and external stresses, including varying environments and different flow compositions. This leads to varying corrosion rates and leak sites at different locations along the pipeline. It is difficult to get an accurate assessment of actual corrosion rates and leak rates because there are so many uncertainties and different measurement techniques used. The often results in a false prediction based on ineffective methodology which lowers reliability.
- To overcome these problems and provide a more accurate predictive method, the method of the invention takes into consideration a number of measured parameters associated with the pipeline and then compares these measurements against past data to predict the probability of corrosion and potential leakage sites.
- An important aspect of the method of the invention is that it is based on an Al algorithm. This makes the predictive function more accurate and reliable because the past data used for comparison is updated on a continuous basis as additional data and experience is obtained. Therefore, the method of the invention provides a continuously updated predictive model and provides better and continuously improving reliability standards.
- As noted, there are numerous stresses on a pipeline that can change corrosion rates and corresponding leak sites along the length of the pipeline. These stresses may be external, such as environmental differences, or internal, such as compositional differences of the pipeline materials or of the material being transported. Different environments are generally related to the physical location of the pipeline and may result from different elevations, inclinations, general weather patterns, temperatures, etc. The material differences can include gas composition, pressure, flow rates, fluid and gas velocities, etc.
- The Figure is a flow chart showing the data and previously generated data being evaluated by various algorithmic parameters to compare differences.
- It is these differences as well as others that can be measured according to the invention and provide the data upon which predictions are made. Therefore, according to the method of the invention, data such as that noted above is gathered. This gathered data is then compared to known data and the comparison is used to predict the areas along the pipeline where corrosion rates may be elevated and that might result in leaks sites.
- A number of modeling methods may be used to perform the comparison. These modeling methods include the De-Waard Model, the Norsok Model and the Leak Rate Model. The model of De-Waard & co-workers was developed based on carbon dioxide corrosion prediction in pipelines. For many years this was the only and most widely used model for the prediction of worst case scenario of carbon dioxide transport. This model is based on empirical fitting of laboratory experimental data and has been revised several times in the last 25 years as new information became available. Several different correction factors have been added to the original equation for different possible scenarios of materials and presence of moisture, PH, corrosion products and oil/liquid wetting. This model does not account for the use of protective corrosion films. Several Operating & Oil & Gas companies have created adaptations of this model and are actively using it in the field for making predictions such as BPs' Cassandra tool. Though this is a good first line model, the performance in predicting corrosion is not very satisfactory.
- For example, during the process of internal corrosion direct assessment (ICDA) processing of seven pipelines, the commonly used De-Waard 95 model failed to predict the corrosion rate for 74.3% of excavation points. So 86 out of 116 excavation points yielded an absolute error greater than 0.05 mm/yr. Similarly, the Norsok M-506 model is an empirical model developed by Norwegian Oil company StetOil. This model is fitted to a large amount of laboratory data and provides a predictive equation for the assessment of corrosion. This model extends the range of predictions and can be used for higher temperatures (150° C.) and higher PH values. But like the De-Waard model, it has serious limitations in predicting corrosion along the pipeline.
- The leak rate models are based on accounting for a multitude of phenomenon in the transport of hydrocarbons such as diffusion theory, fluid mechanics, numerical methods, medium flow state as well the characteristics and nature of leakage holes or spots. These different underlying transport phenomena are modeled and solved through appropriate numerical methods. For example, a hole-tube integrated model has been used to predict the media leakage rate for long-distance NG pipelines. These models are used for risk assessment and integrity management of onshore pipelines but they have serious limitations as far accuracy and reliability is concerned. Therefore, operators are forced to use other available methods like pigging, LDAR, etc., for ensuring a proper risk assessment and integrity management.
- The invention can also be explained with reference to the Figure, which shows a schematic representation of the method. As shown, the input from one or more sources as listed in the Input Data box of the Figure are initially gathered. The physical data may be gathered from sensors or the like deployed along the length of the pipeline. The gathered data is then compared to past known data stored for example in a database. If the gathered data is “normal” meaning that the data is consistent with past data points, then corrosion rates and leak rates can be predicted by using know deterministic models, such as the De-Waard model.
- However, if the gathered data is not consistent with the past data from the database, then the method of the invention utilizes a learning network in order to update the database and thereby increase the reliability and predictability for the system. The Figure shows the proposed architecture and method to predict corrosion and leak rates according to the invention.
- In the method according to the invention, inputs from sensors as well currently employed deterministic models to make predictions are received. The inputs are compared with a deviation filter which is the initiation point for the AA approach. If values are in normal range the AA scheme will not be initiated, but if abnormal deviations are detected, a screening subroutine will start the computational sequence of inventive process. At the heart of this process is an Artificial Neural Network (ANN, alternatively NN) that is composed of a number of neuron layers. The basic or input layer is fed with selected input variables which are then passed into the hidden layers in which the processing will take place. For example, algorithms such as the Levenberg-Marquardt or Back-Propagation algorithms may be applied. The processing takes place in these hidden layers and as output is generated the last hidden layer communicates the results to an external source which generates an output vector of possible disorders such as difference in leak rates, corrosion rates, pressures, flow rates etc.
- The performance of the ANN is dependent upon the number of layers, the number of neurons in each layer, the weights between the related neurons and threshold. These parameters are obtained through subsequent training of the ANN system. The training of an ANN system is a complex task and could lead to predictions which are not relevant. Therefore, according to the invention, the parameters are computed by an intelligent optimization algorithm of evolutionary origin, for example, a Genetic Algorithm (GA).
- The constant interaction of the GA with the ANN system ensures minimization of error and more realistic computation of parameters required for the ANN system. The output from this hybrid system provides the leak and corrosion rates along the pipeline. This output may be sent directly to an operator to make decisions about risks and asset integrity. However, according to the invention, in order to further refine the predictions, a Fuzzy logic subroutine is activated and data is further scrutinized for better and crisper predictions. This can be carried out using a T2FL (
type 2 fuzzy logic system). The T2FL system is made up of a Fuzzifier, a Rule based inference engine and output processor. Therefore, the system integrates the experience and knowledge of very experienced human pipeline operators/experts into the parameters associated with the rule base and membership functions. This helps less experienced operators with key decision making processes. - The fuzzy logic system can be thought of as an average human beings' feelings and inference processing. The fuzzy logic system or strategy is a range-to-point or range-to-range control that differs from the standard control theory of point-to-point control. The output of a fuzzy controller is derived from fuzzifications of both inputs and outputs using the associated member functions. A crisp input will be converted into different members of associated member functions, which than is processed as a range of inputs. The fuzzy logic system implemented by the invention here has three main components.
- Fuzzification: Conversion of classical data or crisp data into fuzzy data or membership function;
- Fuzzy Inference Process: Combines membership functions with rules to derive the fuzzy output;
- Defuzzification: Uses different methods to compute each associated output and puts them into a lookup table, with the process then picking an output from the lookup table based on current inputs from the ANN system.
- The output from the method of the invention may be used to predict corrosion rates and possible specific leak sites along a pipeline. This then allows more accurate preventive maintenance to be performed that can serve to avoid an actual leak from occurring. The method of the invention therefore provides a way of preventing leaks from occurring. This then results in significant cost savings as well as avoiding additional issues that can arise if a leak occurs, e.g. environmental pollution, loss of natural gas, shut down and repair costs, etc.
- It is anticipated that other embodiments and variations of the present invention will become readily apparent to the skilled artisan in the light of the foregoing description, and it is intended that such embodiments and variations likewise be included within the scope of the invention as set out in the appended claims.
Claims (17)
1. A method for predicting corrosion in a pipeline wherein the pipeline is subject to corrosion comprising the steps of obtaining data of parameters associated with the pipeline, comparing the data against previously generated data of parameters associated with the pipeline, and calculating, based on differences between the data with the previously generated data the probability of corrosion in the pipeline.
2. The method as claimed in claim 1 wherein the pipeline contains a hydrocarbon selected from the group consisting of liquid hydrocarbons and natural gas.
3. The method as claimed in claim 1 wherein the data of parameters associated with the pipeline are selected from the group consisting of external stresses and internal stresses.
4. The method as claimed in claim 3 wherein the external stresses are selected from the group consisting of elevations, inclinations, weather patterns, external temperatures.
5. The method as claimed in claim 3 wherein the internal stresses are selected from the group consisting of hydrocarbon composition, gas composition, pressure, flow rates, fluid and gas and hydrocarbon velocities.
6. The method as claimed in claim 1 wherein the probability of corrosion is for a location along the pipeline.
7. The method as claimed in claim 1 wherein the predicting of the probability of corrosion in the pipeline is performed by an artificial intelligence algorithm.
8. The method as claimed in claim 1 wherein the previously generated data of parameters is continuously updated for the pipeline.
9. The method as claimed in claim 7 wherein the artificial intelligence algorithm is a continuously updated predictive model.
10. The method as claimed in claim 7 wherein the artificial intelligence algorithm is selected from the group consisting of De-Waard Model, Norsok Model and the Leak Rate Model.
11. The method as claimed in claim 10 wherein if the data of parameters associated with the pipeline is consistent with previously generated data of parameters associated with the pipeline then this is a normal condition and corrosion and leak rates are predicted using a deterministic model.
12. The method as claimed in claim 1 wherein if the data of parameters associated with the pipeline is not consistent with previously generated data of parameters associated with the pipeline then this is a learning condition and corrosion and leak rates are predicted based upon a generic algorithm.
13. The method as claimed in claim 12 wherein the generic algorithm interacts with an artificial neural network in a continuous manner to generate the prediction of leaks and corrosion rates in the pipeline.
14. The method as claimed in claim 12 wherein the predictions are further refined in a fuzzy logic subroutine algorithm.
15. The method as claimed in claim 12 wherein the fuzzy logic subroutine algorithm is a type 2 fuzzy logic system.
16. The method as claimed in claim 12 wherein the type 2 fuzzy logic system comprises fuzzification, a fuzzy inference process and defuzzification.
17. The method as claimed in claim 11 wherein the deterministic model the De-Waard Model.
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
US15/840,535 US20180365555A1 (en) | 2016-12-22 | 2017-12-13 | Artificial intelligence based algorithm for predicting pipeline leak and corrosion detection |
Applications Claiming Priority (2)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
US201662437716P | 2016-12-22 | 2016-12-22 | |
US15/840,535 US20180365555A1 (en) | 2016-12-22 | 2017-12-13 | Artificial intelligence based algorithm for predicting pipeline leak and corrosion detection |
Publications (1)
Publication Number | Publication Date |
---|---|
US20180365555A1 true US20180365555A1 (en) | 2018-12-20 |
Family
ID=64658229
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
US15/840,535 Abandoned US20180365555A1 (en) | 2016-12-22 | 2017-12-13 | Artificial intelligence based algorithm for predicting pipeline leak and corrosion detection |
Country Status (1)
Country | Link |
---|---|
US (1) | US20180365555A1 (en) |
Cited By (27)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN110043808A (en) * | 2019-05-29 | 2019-07-23 | 浙江大学 | Water supply network leakage monitoring method for early warning based on time series analysis |
CN111222281A (en) * | 2020-02-06 | 2020-06-02 | 中国石油天然气集团有限公司 | Gas reservoir type gas storage injection-production string erosion failure risk determination method |
CN111412391A (en) * | 2019-01-04 | 2020-07-14 | 合肥暖流信息科技有限公司 | Pipe network leakage detection method and system |
CN111637367A (en) * | 2020-04-24 | 2020-09-08 | 西南石油大学 | Detection and evaluation method for corrosion defects in mountain gas transmission pipeline |
CN112487631A (en) * | 2020-11-25 | 2021-03-12 | 中国科学院力学研究所 | Intelligent identification method for working condition parameters of transverse landslide buried pipeline |
WO2021133265A1 (en) * | 2019-12-27 | 2021-07-01 | Ptt Exploration And Production Public Company Limited | A method and system for predicting pipeline corrosion |
CN113160909A (en) * | 2021-04-30 | 2021-07-23 | 苏州华碧微科检测技术有限公司 | Method for rapidly identifying metal corrosion failure |
CN113268883A (en) * | 2021-06-03 | 2021-08-17 | 西安建筑科技大学 | Method for predicting corrosion rate of submarine crude oil pipeline based on PCA-ABC-SVM model |
US20210319098A1 (en) * | 2018-12-31 | 2021-10-14 | Intel Corporation | Securing systems employing artificial intelligence |
CN113551156A (en) * | 2021-06-23 | 2021-10-26 | 广州杰赛科技股份有限公司 | Pipeline state monitoring method and device based on deep learning and storage medium |
CN113803647A (en) * | 2021-08-25 | 2021-12-17 | 浙江工业大学 | Pipeline leakage detection method based on fusion of knowledge characteristics and mixed model |
CN114046456A (en) * | 2021-11-23 | 2022-02-15 | 重庆大学 | Corrosion assessment method and system integrating fuzzy inference and neural network |
US20220065094A1 (en) * | 2020-09-02 | 2022-03-03 | Halliburton Energy Services, Inc. | Identifying Corrosion From Electromagnetic Corrosion Measurements And High-Resolution Circumferential Measurements |
US20220092234A1 (en) * | 2020-09-23 | 2022-03-24 | International Business Machines Corporation | Detection of defects within physical infrastructure by leveraging ai |
WO2022116203A1 (en) * | 2020-12-04 | 2022-06-09 | Cummins Inc. | Systems and methods for utilizing machine learning to monitor vehicle health |
CN114607947A (en) * | 2022-05-13 | 2022-06-10 | 广东力创信息技术有限公司 | Automatic monitoring method and equipment for pipeline leakage |
US20220205353A1 (en) * | 2019-05-16 | 2022-06-30 | Landmark Graphics Corporation | Corrosion Prediction For Integrity Assessment Of Metal Tubular Structures |
US20230021337A1 (en) * | 2020-01-05 | 2023-01-26 | British Telecommunications Public Limited Company | Utilities infratructure selection |
CN115684276A (en) * | 2022-12-28 | 2023-02-03 | 北京华科仪科技股份有限公司 | Desulfurization system pH value prediction method and system based on integrated fusion model |
WO2023019078A1 (en) * | 2021-08-09 | 2023-02-16 | Baker Hughes Holdings Llc | Machine learning based techniques for predicting component corrosion likelihood |
US20230057091A1 (en) * | 2021-08-23 | 2023-02-23 | Saudi Arabian Oil Company | Predicting internal corrosion in gas flow lines using machine learning |
CN115899595A (en) * | 2023-03-08 | 2023-04-04 | 成都秦川物联网科技股份有限公司 | Intelligent gas pipeline corrosion prevention optimization method, internet of things system and storage medium |
CN116336400A (en) * | 2023-05-30 | 2023-06-27 | 克拉玛依市百事达技术开发有限公司 | Baseline detection method for oil and gas gathering and transportation pipeline |
CN116336398A (en) * | 2023-05-24 | 2023-06-27 | 成都秦川物联网科技股份有限公司 | Intelligent gas leakage safety monitoring method, system and medium based on Internet of things |
CN116906839A (en) * | 2023-09-14 | 2023-10-20 | 浙江英集动力科技有限公司 | Safety intelligent monitoring and early warning method for thermodynamic pipeline integrating physical measurement and soft measurement |
US11879599B2 (en) | 2022-12-16 | 2024-01-23 | Chengdu Qinchuan Iot Technology Co., Ltd. | Methods, Internet of Things systems, and mediums for assessing electrochemical corrosion of smart gas pipeline |
WO2024069216A1 (en) * | 2022-09-30 | 2024-04-04 | Matrix Jvco Ltd Trading As Aiq | Method for pipeline corrosion prediction and maintenance |
-
2017
- 2017-12-13 US US15/840,535 patent/US20180365555A1/en not_active Abandoned
Cited By (31)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20210319098A1 (en) * | 2018-12-31 | 2021-10-14 | Intel Corporation | Securing systems employing artificial intelligence |
CN111412391A (en) * | 2019-01-04 | 2020-07-14 | 合肥暖流信息科技有限公司 | Pipe network leakage detection method and system |
US11891889B2 (en) * | 2019-05-16 | 2024-02-06 | Landmark Graphics Corporation | Corrosion prediction for integrity assessment of metal tubular structures |
US20220205353A1 (en) * | 2019-05-16 | 2022-06-30 | Landmark Graphics Corporation | Corrosion Prediction For Integrity Assessment Of Metal Tubular Structures |
CN110043808A (en) * | 2019-05-29 | 2019-07-23 | 浙江大学 | Water supply network leakage monitoring method for early warning based on time series analysis |
WO2021133265A1 (en) * | 2019-12-27 | 2021-07-01 | Ptt Exploration And Production Public Company Limited | A method and system for predicting pipeline corrosion |
US20230021337A1 (en) * | 2020-01-05 | 2023-01-26 | British Telecommunications Public Limited Company | Utilities infratructure selection |
CN111222281A (en) * | 2020-02-06 | 2020-06-02 | 中国石油天然气集团有限公司 | Gas reservoir type gas storage injection-production string erosion failure risk determination method |
CN111637367A (en) * | 2020-04-24 | 2020-09-08 | 西南石油大学 | Detection and evaluation method for corrosion defects in mountain gas transmission pipeline |
US20220065094A1 (en) * | 2020-09-02 | 2022-03-03 | Halliburton Energy Services, Inc. | Identifying Corrosion From Electromagnetic Corrosion Measurements And High-Resolution Circumferential Measurements |
US11781417B2 (en) * | 2020-09-02 | 2023-10-10 | Halliburton Energy Services, Inc. | Identifying corrosion from electromagnetic corrosion measurements and high-resolution circumferential measurements |
US11651119B2 (en) * | 2020-09-23 | 2023-05-16 | International Business Machines Corporation | Detection of defects within physical infrastructure by leveraging AI |
US20220092234A1 (en) * | 2020-09-23 | 2022-03-24 | International Business Machines Corporation | Detection of defects within physical infrastructure by leveraging ai |
CN112487631A (en) * | 2020-11-25 | 2021-03-12 | 中国科学院力学研究所 | Intelligent identification method for working condition parameters of transverse landslide buried pipeline |
WO2022116203A1 (en) * | 2020-12-04 | 2022-06-09 | Cummins Inc. | Systems and methods for utilizing machine learning to monitor vehicle health |
CN113160909A (en) * | 2021-04-30 | 2021-07-23 | 苏州华碧微科检测技术有限公司 | Method for rapidly identifying metal corrosion failure |
CN113268883A (en) * | 2021-06-03 | 2021-08-17 | 西安建筑科技大学 | Method for predicting corrosion rate of submarine crude oil pipeline based on PCA-ABC-SVM model |
CN113551156A (en) * | 2021-06-23 | 2021-10-26 | 广州杰赛科技股份有限公司 | Pipeline state monitoring method and device based on deep learning and storage medium |
WO2023019078A1 (en) * | 2021-08-09 | 2023-02-16 | Baker Hughes Holdings Llc | Machine learning based techniques for predicting component corrosion likelihood |
US20230057091A1 (en) * | 2021-08-23 | 2023-02-23 | Saudi Arabian Oil Company | Predicting internal corrosion in gas flow lines using machine learning |
CN113803647A (en) * | 2021-08-25 | 2021-12-17 | 浙江工业大学 | Pipeline leakage detection method based on fusion of knowledge characteristics and mixed model |
CN114046456A (en) * | 2021-11-23 | 2022-02-15 | 重庆大学 | Corrosion assessment method and system integrating fuzzy inference and neural network |
CN114607947A (en) * | 2022-05-13 | 2022-06-10 | 广东力创信息技术有限公司 | Automatic monitoring method and equipment for pipeline leakage |
WO2024069216A1 (en) * | 2022-09-30 | 2024-04-04 | Matrix Jvco Ltd Trading As Aiq | Method for pipeline corrosion prediction and maintenance |
US11879599B2 (en) | 2022-12-16 | 2024-01-23 | Chengdu Qinchuan Iot Technology Co., Ltd. | Methods, Internet of Things systems, and mediums for assessing electrochemical corrosion of smart gas pipeline |
CN115684276A (en) * | 2022-12-28 | 2023-02-03 | 北京华科仪科技股份有限公司 | Desulfurization system pH value prediction method and system based on integrated fusion model |
CN115899595A (en) * | 2023-03-08 | 2023-04-04 | 成都秦川物联网科技股份有限公司 | Intelligent gas pipeline corrosion prevention optimization method, internet of things system and storage medium |
US11982613B2 (en) | 2023-03-08 | 2024-05-14 | Chengdu Qinchuan Iot Technology Co., Ltd. | Methods and internet of things (IOT) systems for corrosion protection optimization of pipeline of smart gas |
CN116336398A (en) * | 2023-05-24 | 2023-06-27 | 成都秦川物联网科技股份有限公司 | Intelligent gas leakage safety monitoring method, system and medium based on Internet of things |
CN116336400A (en) * | 2023-05-30 | 2023-06-27 | 克拉玛依市百事达技术开发有限公司 | Baseline detection method for oil and gas gathering and transportation pipeline |
CN116906839A (en) * | 2023-09-14 | 2023-10-20 | 浙江英集动力科技有限公司 | Safety intelligent monitoring and early warning method for thermodynamic pipeline integrating physical measurement and soft measurement |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
US20180365555A1 (en) | Artificial intelligence based algorithm for predicting pipeline leak and corrosion detection | |
Bian et al. | Prediction of sulfur solubility in supercritical sour gases using grey wolf optimizer-based support vector machine | |
Shaik et al. | Recurrent neural network-based model for estimating the life condition of a dry gas pipeline | |
Xu et al. | The research progress and prospect of data mining methods on corrosion prediction of oil and gas pipelines | |
Foroozesh et al. | Application of artificial intelligence (AI) in kinetic modeling of methane gas hydrate formation | |
Manan et al. | Failure classification in natural gas pipe-lines using artificial intelligence: A case study | |
Ameli et al. | Modeling interfacial tension of normal alkane-supercritical CO2 systems: Application to gas injection processes | |
Eissa | Unleashing industry 4.0 opportunities: Big data analytics in the midstream oil & gas sector | |
Aditiyawarman et al. | A recent review of risk-based inspection development to support service excellence in the oil and gas industry: an artificial intelligence perspective | |
CN113795799A (en) | Analysis method and apparatus for the same | |
CN113874801A (en) | Analysis method and apparatus for the same | |
CN113874895A (en) | Analysis method and apparatus for the same | |
Castañeda et al. | Expert system through a fuzzy logic approach for the macroscopic visual analysis of corroded metallic ferrous surfaces: Knowledge acquisition process | |
KR102413399B1 (en) | Leak diagnosis system for offshore plant pipelines based on machine learning | |
Sanzo' et al. | Virtual metering and allocation using machine learning algorithms | |
Ganji-Azad et al. | Reservoir fluid PVT properties modeling using adaptive neuro-fuzzy inference systems | |
Kamari et al. | Prediction of maximum possible liquid rates produced from plunger lift by use of a rigorous modeling approach | |
Noorsaman et al. | Machine Learning Algorithms for Failure Prediction Model and Operational Reliability of Onshore Gas Transmission Pipelines. | |
Seneviratne et al. | In-service inspection of static mechanical equipment: Use of a fuzzy inference system for maintaining the quality of an inspection program | |
Gao et al. | Gas outburst prediction based on the intelligent Dempster-Shafer evidence theory | |
Xu et al. | Fuzzy Logic Applications for Water Pipeline Performance Analysis | |
Aggarwal et al. | Automation of Water Distribution System by Prediction of Water Consumption and Leakage Detection Using Machine Learning and IoT | |
Wang et al. | Novel intelligent adjustment height method of Shearer drum based on adaptive fuzzy reasoning Petri net | |
Yang et al. | Machine Learning in Process Safety and Asset Integrity Management | |
Almadhoun et al. | A Novel Hybrid Artificial Intelligence Predictive Multi-Stages Model for Gas Compressors Based on Multi-Factors |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
AS | Assignment |
Owner name: LINDE AKTIENGESELLSCHAFT, GERMANY Free format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNOR:ASLAM, NAVEED;REEL/FRAME:045064/0628 Effective date: 20180226 |
|
STPP | Information on status: patent application and granting procedure in general |
Free format text: DOCKETED NEW CASE - READY FOR EXAMINATION |
|
STPP | Information on status: patent application and granting procedure in general |
Free format text: NON FINAL ACTION MAILED |
|
STCB | Information on status: application discontinuation |
Free format text: ABANDONED -- FAILURE TO RESPOND TO AN OFFICE ACTION |